Dynamics and Control of the Central Carbon Metabolism in Hepatoma Cells

Abstract

BackgroundThe liver plays a major role in metabolism and performs a number of vital functions in the body. Therefore, the determination of hepatic metabolite dynamics and the analysis of the control of the respective biochemical pathways are of great pharmacological and medical importance. Extra- and intracellular time-series data from stimulus-response experiments are gaining in importance in the identification of in vivo metabolite dynamics, while dynamic network models are excellent tools for analyzing complex metabolic control patterns. This is the first study that has been undertaken on the data-driven identification of a dynamic liver central carbon metabolism model and its application in the analysis of the distribution of metabolic control in hepatoma cells.ResultsDynamic metabolite data were collected from HepG2 cells after they had been deprived of extracellular glucose. The concentration of 25 extra- and intracellular intermediates was quantified using HPLC, LC-MS-MS, and GC-MS. The in silico metabolite dynamics were in accordance with the experimental data. The central carbon metabolism of hepatomas was further analyzed with a particular focus on the control of metabolite concentrations and metabolic fluxes. It was observed that the enzyme glucose-6-phosphate dehydrogenase exerted substantial negative control over the glycolytic flux, whereas oxidative phosphorylation had a significant positive control. The control over the rate of NADPH consumption was found to be shared between the NADPH-demand itself (0.65) and the NADPH supply (0.38).ConclusionsBased on time-series data, a dynamic central carbon metabolism model was developed for the investigation of new and complex metabolic control patterns in hepatoma cells. The control patterns found support the hypotheses that the glucose-6-phosphate dehydrogenase and the Warburg effect are promising targets for tumor treatment. The systems-oriented identification of metabolite dynamics is a first step towards the genome-based assessment of potential risks posed by nutrients and drugs.

Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME

“…Beard and Qian (2005) investigated the impairment of hepatic glucose production in von Gierke’s and Hers’ diseases. Maier et al (2010) analyzed the control of glycolysis in HepG2 cells under conditions of glucose deprivation by fitting a kinetic model to the measured concentrations of 25 extra- and intracellular intermediates. A kinetic model of ammonia detoxification in the liver lobule was constructed by Ohno et al (2008) which accounts for the strict zonation of this metabolic function.…”

Isotope labeling experiments in metabolomics and fluxomics

“…From this most simple example it is obvious that neither metabolome nor fluxome measurement alone provide full kinetic description that is necessary to make predictions about rate controlling steps, e.g., carrying out a metabolic control analysis,20 or about regulatory characteristics of the network. Only variations of intracellular concentration and expression levels or fluxes would allow a comprehensive kinetic characterization that is most quickly obtained by dynamic measurements with sufficient perturbation of the system studied 21–23…”

Mass Isotopomer Analysis of Metabolically Labeled Nucleotide Sugars and N- and O-Glycans for Tracing Nucleotide Sugar Metabolisms

“…12 C to 13 C isotopomers, we compared the metabolic flow between Hepa1-6 and pancreatic insulinoma (Min6) cell lines (29,30). We previously found that the hepatic glucose transporter 2 (GLUT2) is highly sialylated (unpublished data).…”